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README.md
In climatekit: Unified Climate Indices for Temperature, Precipitation, and Drought
climatekit
An R package for computing climate indices from daily weather observations. Takes vectors of temperature, precipitation, humidity, and wind data and returns tidy data frames: no file wrangling, no class coercion, no API calls.
What are climate indices?
Climate indices are standardised summary statistics that reduce daily weather observations into meaningful measures of climate conditions. A single year of weather data for one station is 365 rows of temperature, precipitation, wind, and humidity readings. Climate indices compress that into interpretable numbers: how many frost days occurred, how long the growing season lasted, whether the region is in drought.
These indices matter because they are how climate science connects with the real economy. Energy companies use heating and cooling degree days to forecast demand. Agricultural ministries track growing degree days and frost dates. Water authorities monitor SPI and SPEI drought indices. Urban planners measure heat index exceedances. Insurance actuaries count extreme precipitation events. Viticulturists use Huglin and Winkler indices to assess grape-growing potential. Fire services monitor fire weather indices.
The definitions come from international standards bodies - the WMO Expert Team on Climate Change Detection and Indices (ETCCDI) defines 27 core indices, the Expert Team on Sector-specific Climate Indices (ET-SCI) extends these into health, agriculture, and energy domains, and individual research communities have added domain-specific measures like SPEI for drought and Huglin for viticulture.
Getting started: where to get the data
climatekit computes indices from weather data that you provide. It doesn't download anything itself: you bring the data, it does the maths.
If you already have data (a CSV, a database export, an Excel file), all you need is a numeric vector of observations and a date vector:
library(climatekit)
# Read your own data
weather <- read.csv("my_weather_station.csv")
# Compute frost days
ck_frost_days(weather$tmin, weather$date)
If you don't have data yet, the easiest way to get started is with readnoaa, which downloads free daily weather observations from NOAA's global archive of 100,000+ stations. No API key needed:
install.packages("readnoaa") # or devtools::install_github("charlescoverdale/readnoaa")
library(readnoaa)
library(climatekit)
# Step 1: Find a station near you
noaa_nearby(lat = 51.5, lon = -0.1, radius_km = 25)
#> station name latitude longitude distance_km
#> UKE00105915 LONDON WEATHER CENTRE 51.517 -0.117 1.4
# Step 2: Download daily data
weather <- noaa_daily("UKE00105915", "2020-01-01", "2024-12-31",
datatypes = c("TMAX", "TMIN", "PRCP"))
# Step 3: Compute indices
ck_frost_days(weather$tmin, weather$date, period = "annual")
ck_spi(weather$prcp, weather$date, scale = 3)
ck_heating_degree_days((weather$tmax + weather$tmin) / 2, weather$date)
As long as you have a numeric vector and a date vector, climatekit will work with it.
Common data sources
| Region | Source | Coverage | Access |
|---|---|---|---|
| Global | NOAA GHCNd | 100,000+ stations worldwide | Free, no key. Use readnoaa |
| Global | ERA5 reanalysis | Gridded, 0.25° resolution, 1940–present | Free, requires CDS account |
| UK | Met Office MIDAS | ~1,000 UK stations, daily | Free via CEDA, requires registration |
| Europe | ECA&D | 20,000+ stations across Europe | Free download |
| US | ACIS (RCC) | All US cooperative & ASOS stations | Free, no key |
| Australia | Bureau of Meteorology | All BoM stations, daily | Free download |
Why does this package exist?
R has the methods, but they are scattered across half a dozen packages with incompatible interfaces:
| Package | Coverage | Limitation |
|---|---|---|
| ClimInd | 138 indices (SPI, SPEI, heat/cold waves) | Returns raw vectors with no metadata, no dates, no units |
| climdex.pcic | 27 ETCCDI core indices | Requires a custom climdexInput S4 object; locked to ETCCDI standard |
| SPEI | SPI + SPEI drought indices | Single-purpose; only does drought |
| heatwaveR | Marine + atmospheric heatwaves | Single-purpose; only does heatwaves |
| weathermetrics | Unit conversions + heat index | No climate indices |
If you want frost days, degree days, SPI, and the Huglin index in the same analysis, you currently need four packages with four different input formats and four different output structures. One wants an S4 object, another wants a matrix, a third wants separate vectors, and none of them return a data frame with dates attached.
climatekit replaces all of that with a single interface: vectors in, data frames out. Every function takes the same kind of input (numeric vector + date vector), every function returns the same kind of output (a data frame with period, value, index, and unit columns), and the 50+ indices span temperature, precipitation, drought, agroclimatic, and comfort categories.
# Without climatekit: four packages, four input formats, four output structures
library(climdex.pcic)
ci <- climdexInput.raw(tmax = ..., tmin = ..., prec = ..., ...) # S4 object
fd <- climdex.fd(ci) # returns named numeric vector, no dates
library(SPEI)
spi_result <- spi(ts(monthly_precip, frequency = 12), 3) # returns S4, needs ts()
library(ClimInd)
gdd <- gdd(tavg_vector, 10) # returns raw numeric, no metadata
# With climatekit: one package, one interface
library(climatekit)
ck_frost_days(tmin, dates) # → data.frame
ck_spi(precip, dates, scale = 3) # → data.frame
ck_growing_degree_days(tavg, dates, base = 10) # → data.frame
ck_huglin(tmin, tmax, dates, lat = 45) # → data.frame
Installation
install.packages("climatekit")
# Or install the development version from GitHub
# install.packages("devtools")
devtools::install_github("charlescoverdale/climatekit")
Functions
ETCCDI canonical 27
The full ETCCDI core set (Alexander et al. 2006; Zhang et al. 2011) is implemented. ck_etccdi_27() returns an audit table mapping every code to its climatekit function.
| Code | Function | Description |
|---|---|---|
| FD | ck_frost_days() | Days where Tmin < 0 °C |
| ID | ck_ice_days() | Days where Tmax < 0 °C |
| SU | ck_summer_days() | Days where Tmax > 25 °C |
| TR | ck_tropical_nights() | Days where Tmin > 20 °C |
| TXx | ck_txx() | Annual / monthly max of Tmax |
| TNx | ck_tnx() | Annual / monthly max of Tmin (warmest night) |
| TXn | ck_txn() | Annual / monthly min of Tmax (coldest day) |
| TNn | ck_tnn() | Annual / monthly min of Tmin (coldest night) |
| DTR | ck_diurnal_range() | Mean daily temperature range |
| GSL | ck_growing_season() | Growing season length |
| TX10p | ck_tx10p() | % cool days (calendar-day base, optional Zhang 2005 bootstrap) |
| TN10p | ck_tn10p() | % cool nights (calendar-day base, optional bootstrap) |
| TX90p | ck_tx90p() | % warm days (calendar-day base, optional bootstrap) |
| TN90p | ck_tn90p() | % warm nights (calendar-day base, optional bootstrap) |
| WSDI | ck_wsdi() | Warm spell duration index |
| CSDI | ck_csdi() | Cold spell duration index |
| RX1day | ck_max_1day_precip() | Max 1-day precipitation |
| RX5day | ck_max_5day_precip() | Max 5-day precipitation |
| SDII | ck_precip_intensity() | Simple daily intensity index |
| R10mm | ck_heavy_precip() (default 10) | Days with precip >= 10 mm |
| R20mm | ck_very_heavy_precip() (default 20) | Days with precip >= 20 mm |
| Rnnmm | ck_heavy_precip(threshold = nn) | Days with precip >= nn mm |
| CDD | ck_dry_days() | Max consecutive dry days |
| CWD | ck_wet_days() | Max consecutive wet days |
| R95p | ck_r95p() | Total precip on very-wet days |
| R99p | ck_r99p() | Total precip on extremely-wet days |
| PRCPTOT | ck_total_precip() | Annual wet-day precip total |
ET-SCI heatwave family
Period of >= 3 consecutive days with TX above the calendar-day 90th percentile, plus cold-wave duals (TN below 10th percentile).
| Code | Function | Description |
|---|---|---|
| HWN | ck_hwn() | Number of distinct heatwave events |
| HWF | ck_hwf() | Total days inside heatwave events |
| HWD | ck_hwd() | Longest heatwave duration |
| HWM | ck_hwm(mode = "excess" / "absolute") | Mean magnitude across event days |
| HWA | ck_hwa(mode = "excess" / "absolute") | Peak magnitude across event days |
| CWN | ck_cwn() | Cold-wave number |
| CWF | ck_cwf() | Cold-wave frequency |
| CWD | ck_cwd() | Cold-wave duration (note: ETCCDI also uses "CWD" for consecutive wet days, which is ck_wet_days) |
| CWM | ck_cwm() | Cold-wave magnitude |
| CWA | ck_cwa() | Cold-wave amplitude |
| EHF | ck_ehf() | Excess Heat Factor (Nairn & Fawcett 2013) |
Drought, evapotranspiration
| Function | Description |
|---|---|
| ck_spi(distribution = "gamma" / "pearsonIII") | Standardized Precipitation Index |
| ck_spei(distribution = "log-logistic" / "gev") | Standardized Precipitation-Evapotranspiration Index |
| ck_pet() | Reference evapotranspiration (Hargreaves) |
| ck_pet_pm() | Reference evapotranspiration (FAO-56 Penman-Monteith) |
Agroclimatic, comfort, energy
| Function | Description |
|---|---|
| ck_huglin(lat) | Huglin heliothermal index (viticulture) |
| ck_winkler() | Winkler index (wine region classification) |
| ck_branas(lat) | Branas hydrothermal index (disease pressure) |
| ck_first_frost(lat) | First autumn frost date (NH or SH) |
| ck_last_frost(lat) | Last spring frost date (NH or SH) |
| ck_growing_degree_days() | Accumulated GDD above base |
| ck_heating_degree_days() | Heating degree days |
| ck_cooling_degree_days() | Cooling degree days |
| ck_warm_spell() | Warm-spell days (series-quantile, simpler variant of WSDI) |
| ck_wind_chill() | Wind chill (Environment Canada / NWS) |
| ck_heat_index() | Heat index (Rothfusz / NWS) |
| ck_humidex() | Canadian humidex |
| ck_fire_danger() | Simplified fire-danger proxy (use cffdrs for full FWI) |
Discovery, dispatch, gridded
| Function | Description |
|---|---|
| ck_etccdi_27() | Canonical ETCCDI 27 audit table |
| ck_catalogue() | Full implementation catalogue |
| ck_browse(sector, standard, search) | Filter the catalogue |
| ck_compute(data, index, ...) | Dispatch any index by name |
| ck_available(), ck_metadata() | Lightweight registry queries |
| ck_convert_temp() | Celsius / Fahrenheit / Kelvin |
| ck_apply_grid(x, fun, dates, ...) | Apply any function over a terra::SpatRaster |
| ck_from_netcdf(path, var) | Thin reader for netCDF input |
| clear_cache() | Clear the user-data cache |
Examples
How many frost days does a location get?
library(climatekit)
# Daily minimum temperatures for a year
dates <- as.Date("2024-01-01") + 0:364
set.seed(42)
tmin <- sin(seq(0, 2 * pi, length.out = 365)) * 15 + 2
# Annual frost days
ck_frost_days(tmin, dates)
#> period value index unit
#> 2024-01-01 132 frost_days days
# Monthly breakdown
ck_frost_days(tmin, dates, period = "monthly")
#> period value index unit
#> 2024-01-01 25 frost_days days
#> 2024-02-01 17 frost_days days
#> 2024-03-01 4 frost_days days
#> ...
How much heating energy does a building need?
# Heating degree days tell energy companies how much heating demand to expect.
# Each degree below the base temperature (default 18C) for each day adds to the total.
tavg <- sin(seq(0, 2 * pi, length.out = 365)) * 12 + 10
ck_heating_degree_days(tavg, dates, period = "monthly")
#> period value index unit
#> 2024-01-01 481.10 heating_degree_days degree-days
#> 2024-02-01 378.49 heating_degree_days degree-days
#> 2024-03-01 244.53 heating_degree_days degree-days
#> ...
# Cooling degree days for air conditioning demand
ck_cooling_degree_days(tavg, dates, base = 22)
Is a region in drought?
# The Standardized Precipitation Index (SPI) fits a gamma distribution to
# monthly precipitation totals over a rolling window, then transforms to
# standard normal deviates. Values below -1 indicate moderate drought,
# below -1.5 severe drought, below -2 extreme drought.
dates_long <- seq(as.Date("2015-01-01"), as.Date("2024-12-31"), by = "day")
set.seed(42)
precip <- rgamma(length(dates_long), shape = 2, rate = 0.5)
spi <- ck_spi(precip, dates_long, scale = 3)
head(spi)
#> period value index unit
#> 2015-03-01 -0.2891577 spi dimensionless
#> 2015-04-01 0.4458927 spi dimensionless
#> ...
# SPEI adds evapotranspiration to capture temperature-driven drought
pet <- ck_pet(tmin, tmax, lat = 51.5, dates = dates)
What wine regions does a climate support?
# The Huglin heliothermal index classifies grape-growing potential:
# < 1500: too cool for viticulture
# 1500-1800: cool climate (Champagne, Mosel)
# 1800-2100: temperate (Burgundy, Oregon)
# 2100-2400: warm (Bordeaux, Napa)
# > 2400: hot (Barossa, Southern Spain)
dates_gs <- seq(as.Date("2024-04-01"), as.Date("2024-09-30"), by = "day")
set.seed(42)
tmin_gs <- rnorm(length(dates_gs), mean = 12, sd = 3)
tmax_gs <- tmin_gs + runif(length(dates_gs), 8, 15)
ck_huglin(tmin_gs, tmax_gs, dates_gs, lat = 45)
#> period value index unit
#> 2024-01-01 2129.284 huglin degree-days
# Winkler index (wine region classification)
tavg_gs <- (tmin_gs + tmax_gs) / 2
ck_winkler(tavg_gs, dates_gs)
When did frost season start and end?
# First and last frost dates matter for agriculture, construction, and transport.
dates_year <- as.Date("2024-01-01") + 0:364
set.seed(42)
tmin_year <- -10 + seq_along(dates_year) * 0.08 + rnorm(365, sd = 4)
ck_last_frost(tmin_year, dates_year)
#> period value date index unit
#> 2024-01-01 120 2024-04-29 last_frost day of year
ck_first_frost(tmin_year, dates_year)
How dangerous is a heatwave?
# The heat index combines temperature and humidity to estimate
# how hot it actually feels. Values above 40C are dangerous.
ck_heat_index(tavg = c(30, 33, 36, 39), humidity = c(60, 65, 70, 75))
#> value index unit
#> 32.94844 heat_index °C
#> 38.67052 heat_index °C
#> 47.57163 heat_index °C
#> 60.56858 heat_index °C
# Wind chill for cold conditions
ck_wind_chill(tavg = c(-5, -10, -15), wind_speed = c(20, 30, 40))
# Fire weather risk
ck_fire_danger(tavg = 35, humidity = 15, wind_speed = 30, precip = 0)
Removing the in-base bias with the Zhang (2005) bootstrap
# The percentile-day indices (TX10p, TN10p, TX90p, TN90p) compute
# thresholds from a reference period (default 1961-1990). For analysis
# years inside the reference period, the year being assessed contributes
# to its own threshold and biases the result toward 10% / 90%. Set
# bootstrap = TRUE to apply Zhang et al. (2005) leave-one-out resampling
# (the canonical climdex.pcic / climpact behaviour). Costs roughly
# N^2 percentile fits for an N-year reference; opt in for attribution
# work spanning the base.
ck_tx10p(tmax, dates, ref_start = 1961L, ref_end = 1990L, bootstrap = TRUE)
Operational heatwave intensity (Excess Heat Factor)
# EHF combines a 3-day mean temperature anomaly above the 95th
# percentile with an acclimatisation term. Positive EHF days are
# heatwave conditions; larger values indicate more severe events.
ck_ehf(tmax, tmin, dates, ref_start = 1961L, ref_end = 1990L,
stat = "max") # peak EHF in year
ck_ehf(tmax, tmin, dates, stat = "n_positive") # count heatwave-condition days
ck_ehf(tmax, tmin, dates, stat = "sum_positive") # severity-weighted total
FAO-56 Penman-Monteith reference evapotranspiration
# ck_pet() is the Hargreaves estimator (Tmin / Tmax / lat only).
# ck_pet_pm() is the international FAO-56 Penman-Monteith standard,
# with optional humidity, wind, solar-radiation, and elevation inputs.
# Sensible FAO-56 fallbacks are used where data are missing.
ck_pet_pm(tmin, tmax, lat = 45, dates = dates,
elev = 200, wind = 2.5,
rh_min = rh_min, rh_max = rh_max)
Computing indices programmatically
# If you are computing many indices over the same dataset, use ck_compute()
# with the index name as a string. This is useful in loops, Shiny apps,
# or any workflow where the index is selected at runtime.
weather <- data.frame(
dates = as.Date("2024-01-01") + 0:364,
tmin = sin(seq(0, 2 * pi, length.out = 365)) * 15 + 2,
tmax = sin(seq(0, 2 * pi, length.out = 365)) * 15 + 12,
precip = rgamma(365, shape = 0.5, rate = 0.2)
)
# Compute any index by name
ck_compute(weather, "frost_days")
ck_compute(weather, "total_precip", period = "monthly")
# See all available indices
ck_available()
#> index category unit
#> frost_days temperature days
#> ice_days temperature days
#> summer_days temperature days
#> tropical_nights temperature days
#> ...
Related packages
| Package | Description |
|---|---|
| readnoaa | NOAA weather and climate data (pairs with climatekit for data acquisition) |
| carbondata | Carbon market data (EU/UK ETS, voluntary registries) |
| cer | Clean Energy Regulator data (Australia) |
| aemo | Australian Energy Market Operator data |
Migrating from climdex.pcic
climdex.pcic (Pacific Climate Impacts Consortium) was for many years the standard R implementation of the canonical ETCCDI 27. It was archived from CRAN in 2023. climatekit covers the same set with a simpler interface:
# climdex.pcic
ci <- climdexInput.raw(tmax, tmin, prec, ..., base.range = c(1961, 1990))
fd <- climdex.fd(ci) # named numeric vector
# climatekit
fd <- ck_frost_days(tmin, dates) # tidy data frame
See vignette("climdex-migration", package = "climatekit") for the full function-name crosswalk and interface-shift notes.
Citation
citation("climatekit")
If you use the package in academic work, please also cite Alexander et al. (2006) and Zhang et al. (2011) (the canonical ETCCDI references), and Zhang et al. (2005) if you use the in-base bootstrap. inst/CITATION and the root-level CITATION.cff provide the bibentries.
Issues
Please report bugs or requests at https://github.com/charlescoverdale/climatekit/issues.
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climatekit
An R package for computing climate indices from daily weather observations. Takes vectors of temperature, precipitation, humidity, and wind data and returns tidy data frames: no file wrangling, no class coercion, no API calls.
What are climate indices?
Climate indices are standardised summary statistics that reduce daily weather observations into meaningful measures of climate conditions. A single year of weather data for one station is 365 rows of temperature, precipitation, wind, and humidity readings. Climate indices compress that into interpretable numbers: how many frost days occurred, how long the growing season lasted, whether the region is in drought.
These indices matter because they are how climate science connects with the real economy. Energy companies use heating and cooling degree days to forecast demand. Agricultural ministries track growing degree days and frost dates. Water authorities monitor SPI and SPEI drought indices. Urban planners measure heat index exceedances. Insurance actuaries count extreme precipitation events. Viticulturists use Huglin and Winkler indices to assess grape-growing potential. Fire services monitor fire weather indices.
The definitions come from international standards bodies - the WMO Expert Team on Climate Change Detection and Indices (ETCCDI) defines 27 core indices, the Expert Team on Sector-specific Climate Indices (ET-SCI) extends these into health, agriculture, and energy domains, and individual research communities have added domain-specific measures like SPEI for drought and Huglin for viticulture.
Getting started: where to get the data
climatekit computes indices from weather data that you provide. It doesn't download anything itself: you bring the data, it does the maths.
If you already have data (a CSV, a database export, an Excel file), all you need is a numeric vector of observations and a date vector:
library(climatekit)
# Read your own data
weather <- read.csv("my_weather_station.csv")
# Compute frost days
ck_frost_days(weather$tmin, weather$date)
If you don't have data yet, the easiest way to get started is with readnoaa, which downloads free daily weather observations from NOAA's global archive of 100,000+ stations. No API key needed:
install.packages("readnoaa") # or devtools::install_github("charlescoverdale/readnoaa")
library(readnoaa)
library(climatekit)
# Step 1: Find a station near you
noaa_nearby(lat = 51.5, lon = -0.1, radius_km = 25)
#> station name latitude longitude distance_km
#> UKE00105915 LONDON WEATHER CENTRE 51.517 -0.117 1.4
# Step 2: Download daily data
weather <- noaa_daily("UKE00105915", "2020-01-01", "2024-12-31",
datatypes = c("TMAX", "TMIN", "PRCP"))
# Step 3: Compute indices
ck_frost_days(weather$tmin, weather$date, period = "annual")
ck_spi(weather$prcp, weather$date, scale = 3)
ck_heating_degree_days((weather$tmax + weather$tmin) / 2, weather$date)
As long as you have a numeric vector and a date vector, climatekit will work with it.
Common data sources
| Region | Source | Coverage | Access |
|---|---|---|---|
| Global | NOAA GHCNd | 100,000+ stations worldwide | Free, no key. Use readnoaa |
| Global | ERA5 reanalysis | Gridded, 0.25° resolution, 1940–present | Free, requires CDS account |
| UK | Met Office MIDAS | ~1,000 UK stations, daily | Free via CEDA, requires registration |
| Europe | ECA&D | 20,000+ stations across Europe | Free download |
| US | ACIS (RCC) | All US cooperative & ASOS stations | Free, no key |
| Australia | Bureau of Meteorology | All BoM stations, daily | Free download |
Why does this package exist?
R has the methods, but they are scattered across half a dozen packages with incompatible interfaces:
| Package | Coverage | Limitation |
|---|---|---|
| ClimInd | 138 indices (SPI, SPEI, heat/cold waves) | Returns raw vectors with no metadata, no dates, no units |
| climdex.pcic | 27 ETCCDI core indices | Requires a custom climdexInput S4 object; locked to ETCCDI standard |
| SPEI | SPI + SPEI drought indices | Single-purpose; only does drought |
| heatwaveR | Marine + atmospheric heatwaves | Single-purpose; only does heatwaves |
| weathermetrics | Unit conversions + heat index | No climate indices |
If you want frost days, degree days, SPI, and the Huglin index in the same analysis, you currently need four packages with four different input formats and four different output structures. One wants an S4 object, another wants a matrix, a third wants separate vectors, and none of them return a data frame with dates attached.
climatekit replaces all of that with a single interface: vectors in, data frames out. Every function takes the same kind of input (numeric vector + date vector), every function returns the same kind of output (a data frame with period, value, index, and unit columns), and the 50+ indices span temperature, precipitation, drought, agroclimatic, and comfort categories.
# Without climatekit: four packages, four input formats, four output structures
library(climdex.pcic)
ci <- climdexInput.raw(tmax = ..., tmin = ..., prec = ..., ...) # S4 object
fd <- climdex.fd(ci) # returns named numeric vector, no dates
library(SPEI)
spi_result <- spi(ts(monthly_precip, frequency = 12), 3) # returns S4, needs ts()
library(ClimInd)
gdd <- gdd(tavg_vector, 10) # returns raw numeric, no metadata
# With climatekit: one package, one interface
library(climatekit)
ck_frost_days(tmin, dates) # → data.frame
ck_spi(precip, dates, scale = 3) # → data.frame
ck_growing_degree_days(tavg, dates, base = 10) # → data.frame
ck_huglin(tmin, tmax, dates, lat = 45) # → data.frame
Installation
install.packages("climatekit")
# Or install the development version from GitHub
# install.packages("devtools")
devtools::install_github("charlescoverdale/climatekit")
Functions
ETCCDI canonical 27
The full ETCCDI core set (Alexander et al. 2006; Zhang et al. 2011) is implemented. ck_etccdi_27() returns an audit table mapping every code to its climatekit function.
| Code | Function | Description |
|---|---|---|
| FD | ck_frost_days() | Days where Tmin < 0 °C |
| ID | ck_ice_days() | Days where Tmax < 0 °C |
| SU | ck_summer_days() | Days where Tmax > 25 °C |
| TR | ck_tropical_nights() | Days where Tmin > 20 °C |
| TXx | ck_txx() | Annual / monthly max of Tmax |
| TNx | ck_tnx() | Annual / monthly max of Tmin (warmest night) |
| TXn | ck_txn() | Annual / monthly min of Tmax (coldest day) |
| TNn | ck_tnn() | Annual / monthly min of Tmin (coldest night) |
| DTR | ck_diurnal_range() | Mean daily temperature range |
| GSL | ck_growing_season() | Growing season length |
| TX10p | ck_tx10p() | % cool days (calendar-day base, optional Zhang 2005 bootstrap) |
| TN10p | ck_tn10p() | % cool nights (calendar-day base, optional bootstrap) |
| TX90p | ck_tx90p() | % warm days (calendar-day base, optional bootstrap) |
| TN90p | ck_tn90p() | % warm nights (calendar-day base, optional bootstrap) |
| WSDI | ck_wsdi() | Warm spell duration index |
| CSDI | ck_csdi() | Cold spell duration index |
| RX1day | ck_max_1day_precip() | Max 1-day precipitation |
| RX5day | ck_max_5day_precip() | Max 5-day precipitation |
| SDII | ck_precip_intensity() | Simple daily intensity index |
| R10mm | ck_heavy_precip() (default 10) | Days with precip >= 10 mm |
| R20mm | ck_very_heavy_precip() (default 20) | Days with precip >= 20 mm |
| Rnnmm | ck_heavy_precip(threshold = nn) | Days with precip >= nn mm |
| CDD | ck_dry_days() | Max consecutive dry days |
| CWD | ck_wet_days() | Max consecutive wet days |
| R95p | ck_r95p() | Total precip on very-wet days |
| R99p | ck_r99p() | Total precip on extremely-wet days |
| PRCPTOT | ck_total_precip() | Annual wet-day precip total |
ET-SCI heatwave family
Period of >= 3 consecutive days with TX above the calendar-day 90th percentile, plus cold-wave duals (TN below 10th percentile).
| Code | Function | Description |
|---|---|---|
| HWN | ck_hwn() | Number of distinct heatwave events |
| HWF | ck_hwf() | Total days inside heatwave events |
| HWD | ck_hwd() | Longest heatwave duration |
| HWM | ck_hwm(mode = "excess" / "absolute") | Mean magnitude across event days |
| HWA | ck_hwa(mode = "excess" / "absolute") | Peak magnitude across event days |
| CWN | ck_cwn() | Cold-wave number |
| CWF | ck_cwf() | Cold-wave frequency |
| CWD | ck_cwd() | Cold-wave duration (note: ETCCDI also uses "CWD" for consecutive wet days, which is ck_wet_days) |
| CWM | ck_cwm() | Cold-wave magnitude |
| CWA | ck_cwa() | Cold-wave amplitude |
| EHF | ck_ehf() | Excess Heat Factor (Nairn & Fawcett 2013) |
Drought, evapotranspiration
| Function | Description |
|---|---|
| ck_spi(distribution = "gamma" / "pearsonIII") | Standardized Precipitation Index |
| ck_spei(distribution = "log-logistic" / "gev") | Standardized Precipitation-Evapotranspiration Index |
| ck_pet() | Reference evapotranspiration (Hargreaves) |
| ck_pet_pm() | Reference evapotranspiration (FAO-56 Penman-Monteith) |
Agroclimatic, comfort, energy
| Function | Description |
|---|---|
| ck_huglin(lat) | Huglin heliothermal index (viticulture) |
| ck_winkler() | Winkler index (wine region classification) |
| ck_branas(lat) | Branas hydrothermal index (disease pressure) |
| ck_first_frost(lat) | First autumn frost date (NH or SH) |
| ck_last_frost(lat) | Last spring frost date (NH or SH) |
| ck_growing_degree_days() | Accumulated GDD above base |
| ck_heating_degree_days() | Heating degree days |
| ck_cooling_degree_days() | Cooling degree days |
| ck_warm_spell() | Warm-spell days (series-quantile, simpler variant of WSDI) |
| ck_wind_chill() | Wind chill (Environment Canada / NWS) |
| ck_heat_index() | Heat index (Rothfusz / NWS) |
| ck_humidex() | Canadian humidex |
| ck_fire_danger() | Simplified fire-danger proxy (use cffdrs for full FWI) |
Discovery, dispatch, gridded
| Function | Description |
|---|---|
| ck_etccdi_27() | Canonical ETCCDI 27 audit table |
| ck_catalogue() | Full implementation catalogue |
| ck_browse(sector, standard, search) | Filter the catalogue |
| ck_compute(data, index, ...) | Dispatch any index by name |
| ck_available(), ck_metadata() | Lightweight registry queries |
| ck_convert_temp() | Celsius / Fahrenheit / Kelvin |
| ck_apply_grid(x, fun, dates, ...) | Apply any function over a terra::SpatRaster |
| ck_from_netcdf(path, var) | Thin reader for netCDF input |
| clear_cache() | Clear the user-data cache |
Examples
How many frost days does a location get?
library(climatekit)
# Daily minimum temperatures for a year
dates <- as.Date("2024-01-01") + 0:364
set.seed(42)
tmin <- sin(seq(0, 2 * pi, length.out = 365)) * 15 + 2
# Annual frost days
ck_frost_days(tmin, dates)
#> period value index unit
#> 2024-01-01 132 frost_days days
# Monthly breakdown
ck_frost_days(tmin, dates, period = "monthly")
#> period value index unit
#> 2024-01-01 25 frost_days days
#> 2024-02-01 17 frost_days days
#> 2024-03-01 4 frost_days days
#> ...
How much heating energy does a building need?
# Heating degree days tell energy companies how much heating demand to expect.
# Each degree below the base temperature (default 18C) for each day adds to the total.
tavg <- sin(seq(0, 2 * pi, length.out = 365)) * 12 + 10
ck_heating_degree_days(tavg, dates, period = "monthly")
#> period value index unit
#> 2024-01-01 481.10 heating_degree_days degree-days
#> 2024-02-01 378.49 heating_degree_days degree-days
#> 2024-03-01 244.53 heating_degree_days degree-days
#> ...
# Cooling degree days for air conditioning demand
ck_cooling_degree_days(tavg, dates, base = 22)
Is a region in drought?
# The Standardized Precipitation Index (SPI) fits a gamma distribution to
# monthly precipitation totals over a rolling window, then transforms to
# standard normal deviates. Values below -1 indicate moderate drought,
# below -1.5 severe drought, below -2 extreme drought.
dates_long <- seq(as.Date("2015-01-01"), as.Date("2024-12-31"), by = "day")
set.seed(42)
precip <- rgamma(length(dates_long), shape = 2, rate = 0.5)
spi <- ck_spi(precip, dates_long, scale = 3)
head(spi)
#> period value index unit
#> 2015-03-01 -0.2891577 spi dimensionless
#> 2015-04-01 0.4458927 spi dimensionless
#> ...
# SPEI adds evapotranspiration to capture temperature-driven drought
pet <- ck_pet(tmin, tmax, lat = 51.5, dates = dates)
What wine regions does a climate support?
# The Huglin heliothermal index classifies grape-growing potential:
# < 1500: too cool for viticulture
# 1500-1800: cool climate (Champagne, Mosel)
# 1800-2100: temperate (Burgundy, Oregon)
# 2100-2400: warm (Bordeaux, Napa)
# > 2400: hot (Barossa, Southern Spain)
dates_gs <- seq(as.Date("2024-04-01"), as.Date("2024-09-30"), by = "day")
set.seed(42)
tmin_gs <- rnorm(length(dates_gs), mean = 12, sd = 3)
tmax_gs <- tmin_gs + runif(length(dates_gs), 8, 15)
ck_huglin(tmin_gs, tmax_gs, dates_gs, lat = 45)
#> period value index unit
#> 2024-01-01 2129.284 huglin degree-days
# Winkler index (wine region classification)
tavg_gs <- (tmin_gs + tmax_gs) / 2
ck_winkler(tavg_gs, dates_gs)
When did frost season start and end?
# First and last frost dates matter for agriculture, construction, and transport.
dates_year <- as.Date("2024-01-01") + 0:364
set.seed(42)
tmin_year <- -10 + seq_along(dates_year) * 0.08 + rnorm(365, sd = 4)
ck_last_frost(tmin_year, dates_year)
#> period value date index unit
#> 2024-01-01 120 2024-04-29 last_frost day of year
ck_first_frost(tmin_year, dates_year)
How dangerous is a heatwave?
# The heat index combines temperature and humidity to estimate
# how hot it actually feels. Values above 40C are dangerous.
ck_heat_index(tavg = c(30, 33, 36, 39), humidity = c(60, 65, 70, 75))
#> value index unit
#> 32.94844 heat_index °C
#> 38.67052 heat_index °C
#> 47.57163 heat_index °C
#> 60.56858 heat_index °C
# Wind chill for cold conditions
ck_wind_chill(tavg = c(-5, -10, -15), wind_speed = c(20, 30, 40))
# Fire weather risk
ck_fire_danger(tavg = 35, humidity = 15, wind_speed = 30, precip = 0)
Removing the in-base bias with the Zhang (2005) bootstrap
# The percentile-day indices (TX10p, TN10p, TX90p, TN90p) compute
# thresholds from a reference period (default 1961-1990). For analysis
# years inside the reference period, the year being assessed contributes
# to its own threshold and biases the result toward 10% / 90%. Set
# bootstrap = TRUE to apply Zhang et al. (2005) leave-one-out resampling
# (the canonical climdex.pcic / climpact behaviour). Costs roughly
# N^2 percentile fits for an N-year reference; opt in for attribution
# work spanning the base.
ck_tx10p(tmax, dates, ref_start = 1961L, ref_end = 1990L, bootstrap = TRUE)
Operational heatwave intensity (Excess Heat Factor)
# EHF combines a 3-day mean temperature anomaly above the 95th
# percentile with an acclimatisation term. Positive EHF days are
# heatwave conditions; larger values indicate more severe events.
ck_ehf(tmax, tmin, dates, ref_start = 1961L, ref_end = 1990L,
stat = "max") # peak EHF in year
ck_ehf(tmax, tmin, dates, stat = "n_positive") # count heatwave-condition days
ck_ehf(tmax, tmin, dates, stat = "sum_positive") # severity-weighted total
FAO-56 Penman-Monteith reference evapotranspiration
# ck_pet() is the Hargreaves estimator (Tmin / Tmax / lat only).
# ck_pet_pm() is the international FAO-56 Penman-Monteith standard,
# with optional humidity, wind, solar-radiation, and elevation inputs.
# Sensible FAO-56 fallbacks are used where data are missing.
ck_pet_pm(tmin, tmax, lat = 45, dates = dates,
elev = 200, wind = 2.5,
rh_min = rh_min, rh_max = rh_max)
Computing indices programmatically
# If you are computing many indices over the same dataset, use ck_compute()
# with the index name as a string. This is useful in loops, Shiny apps,
# or any workflow where the index is selected at runtime.
weather <- data.frame(
dates = as.Date("2024-01-01") + 0:364,
tmin = sin(seq(0, 2 * pi, length.out = 365)) * 15 + 2,
tmax = sin(seq(0, 2 * pi, length.out = 365)) * 15 + 12,
precip = rgamma(365, shape = 0.5, rate = 0.2)
)
# Compute any index by name
ck_compute(weather, "frost_days")
ck_compute(weather, "total_precip", period = "monthly")
# See all available indices
ck_available()
#> index category unit
#> frost_days temperature days
#> ice_days temperature days
#> summer_days temperature days
#> tropical_nights temperature days
#> ...
Related packages
| Package | Description |
|---|---|
| readnoaa | NOAA weather and climate data (pairs with climatekit for data acquisition) |
| carbondata | Carbon market data (EU/UK ETS, voluntary registries) |
| cer | Clean Energy Regulator data (Australia) |
| aemo | Australian Energy Market Operator data |
Migrating from climdex.pcic
climdex.pcic (Pacific Climate Impacts Consortium) was for many years the standard R implementation of the canonical ETCCDI 27. It was archived from CRAN in 2023. climatekit covers the same set with a simpler interface:
# climdex.pcic
ci <- climdexInput.raw(tmax, tmin, prec, ..., base.range = c(1961, 1990))
fd <- climdex.fd(ci) # named numeric vector
# climatekit
fd <- ck_frost_days(tmin, dates) # tidy data frame
See vignette("climdex-migration", package = "climatekit") for the full function-name crosswalk and interface-shift notes.
Citation
citation("climatekit")
If you use the package in academic work, please also cite Alexander et al. (2006) and Zhang et al. (2011) (the canonical ETCCDI references), and Zhang et al. (2005) if you use the in-base bootstrap. inst/CITATION and the root-level CITATION.cff provide the bibentries.
Issues
Please report bugs or requests at https://github.com/charlescoverdale/climatekit/issues.
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